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Wos Paper Detail

Community
cookjohn
wos-paper-detail

Get detailed information for a paper by WoS ID (e.g., WOS:000779183600001).

Overview

Publishercookjohn
Repositorywos-skills
Skill namewos-paper-detail
Stars
133
Forks
17
Bundled files
Instructions only
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by cookjohn on GitHub. Read the source before you install it.

Installation

Install the Wos Paper Detail AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/cookjohn/wos-skills/tree/main/.claude/skills/wos-paper-detail
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/cookjohn/wos-skills.git /tmp/wos-skills
mkdir -p .claude/skills
cp -r /tmp/wos-skills/.claude/skills/wos-paper-detail .claude/skills/wos-paper-detail
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Wos Paper Detail in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Wos Paper Detail on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Wos Paper Detail is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

WoS Paper Detail

Navigate to a WoS full record page and extract comprehensive paper metadata.

Steps

Step 1: Parse Input

Accept either:

  • WoS Accession Number: WOS:000779183600001
  • Full URL: https://www.webofscience.com/wos/woscc/full-record/WOS:000779183600001

Extract the WoS ID if a full URL is provided.

Step 2: Navigate

navigate_page({
  url: "{BASE_URL}/wos/woscc/full-record/{WOS_ID}",
  initScript: "Object.defineProperty(navigator, 'webdriver', {get: () => undefined})"
})

Step 3: Extract Full Record Data

javascript
async () => {
  // Wait for full record to load
  for (let i = 0; i < 30; i++) {
    if (document.querySelector('[data-ta="FullRTa-fullRecordtitle-0"]')) break;
    await new Promise(r => setTimeout(r, 500));
  }
  await new Promise(r => setTimeout(r, 1500));

  // Check access
  if (document.body.innerText.includes('Sign in') && !document.querySelector('[data-ta="FullRTa-fullRecordtitle-0"]')) {
    return { status: 'login_required' };
  }

  // Helper: get value for a labeled field (h3 label -> parent's non-h3 children)
  const getField = (label) => {
    const h3 = [...document.querySelectorAll('h3')].find(h => h.textContent.trim() === label);
    if (!h3) return '';
    const parent = h3.parentElement;
    const valueEls = [...parent.children].filter(el => el.tagName !== 'H3');
    return valueEls.map(s => s.textContent?.trim()).join(' ').substring(0, 300) || '';
  };

  // Title
  const title = document.querySelector('[data-ta="FullRTa-fullRecordtitle-0"]')?.textContent?.trim() || '';

  // Authors
  const authors = [...document.querySelectorAll('[data-ta^="SumAuthTa-DisplayName-author-en"]')]
    .map(a => a.textContent?.trim());

  // Abstract
  const abstract = document.querySelector('div.abstract--instance')?.textContent?.trim() || '';

  // Source/Journal - extract only journal name from the JCR sidenav component
  let source = '';
  const sourceSpan = document.querySelector('h3[data-ta="FullRTa-sourceLabel"]')?.parentElement?.querySelector('span.section-label-data');
  if (sourceSpan) {
    // The span contains app-jcr-sidenav with journal name + popup. Get first text content before "arrow_back"
    const fullText = sourceSpan.textContent?.trim() || '';
    source = fullText.split('arrow_back')[0].trim();
  }
  if (!source) {
    source = document.querySelector('.summary-source-title-link')?.firstChild?.textContent?.trim() || '';
  }

  // DOI - try link first, fall back to h3 label extraction
  const doiEl = document.querySelector('a[href*="doi.org"]');
  const doi = doiEl?.textContent?.trim() || doiEl?.href?.replace(/.*doi\.org\//, '') || getField('DOI');

  // Metadata fields
  const volume = getField('Volume');
  const issue = getField('Issue');
  const pages = getField('Page');
  const published = getField('Published');
  const earlyAccess = getField('Early Access');
  const docType = getField('Document Type');
  const language = getField('Language');
  const accessionNumber = getField('Accession Number');
  const issn = getField('ISSN');
  const eissn = getField('eISSN');

  // Keywords - use parentElement.querySelectorAll('a') to get ALL keyword links
  const getKeywords = (label) => {
    const h3 = [...document.querySelectorAll('h3')].find(h => h.textContent.trim() === label);
    if (!h3) return [];
    return [...h3.parentElement.querySelectorAll('a')].map(a => a.textContent?.trim()).filter(Boolean);
  };
  const authorKeywords = getKeywords('Author Keywords');
  const keywordsPlus = getKeywords('Keywords Plus');

  // Categories
  const researchAreas = getField('Research Areas');
  const wosCategories = getField('Web of Science Categories');

  // Citations - full record page uses FullRRPTa selectors, NOT stat-number selectors
  const citedLinks = [...document.querySelectorAll('a[data-ta*="times-cited-count-link"]')];
  // WOSCC link text is "992 In Web of Science Core Collection" - extract leading number
  const extractNum = (text) => (text?.match(/^[\d,]+/) || ['0'])[0];
  const wosccCited = extractNum(citedLinks.find(a => a.getAttribute('data-ta')?.includes('-WOSCC'))?.textContent?.trim());
  const alldbCited = extractNum(citedLinks.find(a => a.getAttribute('data-ta')?.includes('-ALLDB'))?.textContent?.trim());
  const mainCited = extractNum(citedLinks.find(a => /^\d/.test(a.textContent?.trim()))?.textContent?.trim());
  const citedCount = wosccCited || mainCited || '0';

  // References
  const refsCount = document.querySelector('a[data-ta*="refCountLink"]')?.textContent?.trim() || '0';

  // JIF - extract from JCR popup/sidenav text content
  let jif = '', jifYear = '', jifFiveYear = '', jcrQuartile = '';
  const jcrPopup = document.querySelector('[class*="journal-content"], app-jcr-sidenav');
  if (jcrPopup) {
    const jcrText = jcrPopup.textContent || '';
    // Pattern: "Journal Impact Factor ™20242.8Five Year3.4"
    const jifMatch = jcrText.match(/Impact Factor\s*(?:™|TM)?\s*(\d{4})\s*([\d.]+)/);
    if (jifMatch) { jifYear = jifMatch[1]; jif = jifMatch[2]; }
    const fyMatch = jcrText.match(/Five Year\s*([\d.]+)/);
    if (fyMatch) jifFiveYear = fyMatch[1];
    // Quartile: "159/316 Q3" pattern in JCR summary
    const qMatch = jcrText.match(/\d+\/\d+\s*(Q[1-4])/);
    if (qMatch) jcrQuartile = qMatch[1];
  }

  // Full text links
  const ftLinks = [...document.querySelectorAll('a[data-ta^="FRLinkTa"]')].map(a => ({
    text: a.textContent?.trim()?.replace(/open_in_new/g, '').trim(),
    href: a.href
  })).filter(l => l.text);

  return {
    status: 'ok',
    title, authors, abstract, source, doi,
    volume, issue, pages, published, earlyAccess,
    docType, language, accessionNumber, issn, eissn,
    authorKeywords, keywordsPlus,
    researchAreas, wosCategories,
    citedCount, alldbCited, refsCount,
    jif, jifYear, jifFiveYear, jcrQuartile,
    ftLinks
  };
}

Step 4: Present Results

Format the output as a structured summary:

## {Title}

**Authors**: {authors joined by "; "}
**Source**: {source}, Vol. {volume}, Iss. {issue}, pp. {pages}
**Published**: {published}
**DOI**: {doi}
**Document Type**: {docType}
**WoS ID**: {accessionNumber}

### Abstract
{abstract}

### Keywords
- **Author Keywords**: {authorKeywords}
- **Keywords Plus**: {keywordsPlus}

### Metrics
- **Times Cited (WOSCC)**: {citedCount}
- **Times Cited (All DB)**: {alldbCited}
- **References**: {refsCount}
- **JIF ({jifYear})**: {jif} (Five Year: {jifFiveYear})
- **JCR Quartile**: {jcrQuartile}

### Classification
- **Research Areas**: {researchAreas}
- **WoS Categories**: {wosCategories}
- **ISSN**: {issn} | **eISSN**: {eissn}

### Full Text Links
{list of ftLinks}

Offer next actions:

  • "Use /wos-export {WoS ID} to export this paper"
  • "Use /wos-download {WoS ID} to download PDF"

Key CSS Selectors (Full Record Page)

ElementSelectorNotes
Title[data-ta="FullRTa-fullRecordtitle-0"]
Authors[data-ta^="SumAuthTa-DisplayName-author-en"]
Abstractdiv.abstract--instance
Source labelh3[data-ta="FullRTa-sourceLabel"]Parent span has JCR popup; split on "arrow_back"
DOIa[href*="doi.org"]
Author Keywordsh3#FRkeywordsTa-authorKeywordsLabel → parent a tagsUse parentElement.querySelectorAll('a')
Keywords PlusSame pattern with "Keywords Plus" label
Citations (WOSCC)a[data-ta*="times-cited-count-link-WOSCC"]Full record page only
Citations (All DB)a[data-ta*="times-cited-count-link-ALLDB"]Full record page only
Referencesa[data-ta*="refCountLink"]Full record page only
Metadata fieldsh3 label → parentElement → non-h3 childrenVolume, Issue, Page, Published, etc.
JIFInside app-jcr-sidenav text contentRegex: /Impact Factor\s*™?\s*(\d{4})\s*([\d.]+)/
Full text linksa[data-ta^="FRLinkTa"]

Notes

  • This skill uses 2 tool calls: navigate_page + evaluate_script
  • IMPORTANT: Full record page uses different citation selectors than search results page:
    • Search results: a[data-ta="stat-number-citation-related-count"]
    • Full record: a[data-ta*="times-cited-count-link-WOSCC"]
  • Source extraction must split on "arrow_back" to avoid capturing JCR popup content
  • Keywords must use parentElement.querySelectorAll('a'), NOT nextElementSibling
  • JIF is embedded in JCR sidenav popup text, not in a standalone element

Frequently asked questions

What does the Wos Paper Detail AI skill do?

Get detailed information for a paper by WoS ID (e.g., WOS:000779183600001).

Why use Wos Paper Detail on TypingMind?

Because you install it once and use it with any model. Wos Paper Detail is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Wos Paper Detail in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/cookjohn/wos-skills/tree/main/.claude/skills/wos-paper-detail. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Wos Paper Detail?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Wos Paper Detail?

As many as you like. As long as a model supports skills, you can use Wos Paper Detail with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Wos Paper Detail AI skill free?

It is published on GitHub by cookjohn. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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